LHCb Software Training: Printing and Job options

The purpose of this exercise is to make you familiar with using job options to configure algorithms, and with the methods for printing from within a Gaudi application.

Prerequisites

The instructions assume that you have already followed part 1 of the LHCb software basics tutorial. You should also have looked at the slides "Introduction to Gaudi" [ .ppt] and "Printing and job options" [.ppt] attached to this topic. Please feel free to update these slides if you modify them for a future tutorial session.

These instructions have last been checked against the DaVinci v33r0p1 environment. Please use this version of DaVinci or a more recent version.

Setting up the environment

We will be working in the same environment as the DaVinci tutorial.
  • Choose the DaVinci environment
    setenvDaVinci v33r0p1
  • Check out and configure the Tutorial package
    getpack Tutorial/Analysis v10r4
    cd Tutorial/Analysis
This package contains a requirements file already set up for the tutorial, a set of options and solutions for the DaVinci tutorial sessions, and an empty src directory

Creating a GaudiAlgorithm, adding a property and printing its value

  • Create a new header file using emacs
    emacs src/MyFirstAlgorithm.h &
    Answer " A " to emacs, meaning that you want to create a header file for a (Gaudi)Algorithm. Do not answer "D" for this exercise, DaVinciAlgorithm requires more specialised job options than those we are studying here.
  • Add a member variable to store the property
    double m_jPsiMassWin;
  • Create the corresponding .cpp file
    emacs src/MyFirstAlgorithm.cpp &
  • In the class constructor, declare a name for the property, initialize it to a default value and document it
    declareProperty( "MassWindow", <memberVariable> = <defaultValue>, "Documentation" );
  • Print the property's value twice, using two different units
    info() << "Mass window is " << value << " Units" << endmsg;
  • Save both files and build a library with them

Setting up the job options and running the job

  • Create a job options file
    emacs options/myJob.py &
  • Tell python about the Gaudi framework
    from Gaudi.Configuration import *
  • Tell python about your algorithm
    from Configurables import MyFirstAlgorithm
  • Add an instance of your algorithm to the application
    myAlg = MyFirstAlgorithm()
    ApplicationMgr().TopAlg += [myAlg]
  • Save the file and run the job (see either Exercise 4 or Exercise 6 in part 1 of the tutorial). You should see some printout from your algorithm. If you do not, either your algorithm was not called (check your job options) or you put the printout in the wrong place in the code. Only printout from initialize() will be seen; execute() is not called in this example (why?)

Modify the job behaviour by changing job options

The following examples illustrate how you can change an algorithm's behaviour by changing the job options, without recompiling. Try the following in turn, rerunning the job each time. No need to recompile!
  • Modify the value of the algorithm's property
    from GaudiKernel.SystemOfUnits import GeV, MeV
    myAlg.MassWindow  = 1.3 * GeV
  • One big advantage of using Python for job options is its syntax and type checking. See what happens with each of the following typing errors:
    myAlg = MyFirstAlgorithm()
    myAlg.MasssWindow = 1.3 * GeV
    myAlg.MassWindow = "some string"
  • Change the global output level of the application
    MessageSvc().OutputLevel = DEBUG
    Try any of the values VERBOSE, DEBUG, INFO, WARNING, ERROR
  • Change the output level of your algorithm
    myAlg.OutputLevel = DEBUG
    Try any of the values VERBOSE, DEBUG, INFO, WARNING, ERROR
  • Run two instances of your algorithm, with different values for the cut. Hint:
    anAlg = SomeAlg("Alg1") #instantiates class SomeAlg with instance name "Alg1" and assigns it to python variable anAlg
  • A more detailed discussion of python configurables, including examples for configuring tools, can be found in the TupleToolsAndConfigurables FAQ.

Modify the job behaviour with StatusCode

  • Look at what happens when you change the initialize() method of your algorithm to
    return StatusCode::FAILURE

The LHCb convention is that algorithms should return StatusCode::FAILURE from initialize() if there is a fatal configuration error which makes it pointless to continue with the job. Algorithms should never return StatusCode::FAILURE from inside the event loop ( execute() method), because this will not just stop processing the current event, but will stop the job. Instead, they should trap the error and take any necessary remedial action. There are ways for algorithms to abort processing of the current event only, but these are beyond the scope of this tutorial; detailed instructions are available here

  • Play with the Warning() and Error() methods.
    • What is the difference compared to using the warning() and err() MsgStream functions?.
    • What happens if you
      return Error("An error");
      from your algorithm?
    • And if you
      return Error("Another error", StatusCode::SUCCESS);

It is recommended that all errors are reported using the Warning() or Error() methods, due to the nice feature of printing statistics at the end of the job. It is also good practice that you always print a warning or error before returning StatusCode::FAILURE

Further reading

A more extensive introduction to job configuration using Python is available here and here

-- MarcoCattaneo - 27-Jan-2010

Topic attachments
I Attachment History Action Size Date Who Comment
PowerPointppt Gaudi_Introduction.ppt r14 r13 r12 r11 r10 manage 770.3 K 2011-09-27 - 18:51 MarcoCattaneo Slides: "Introduction to Gaudi"
PowerPointppt Printing_and_Job_Options.ppt r14 r13 r12 r11 r10 manage 128.0 K 2011-09-27 - 18:51 MarcoCattaneo Slides: "Printing and job options"
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Topic revision: r53 - 2011-12-02 - MarcoCattaneo
 
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